A comparative study on prediction of sediment yield in the Euphrates basin.

Agricultural fields’ fertility decays and dam reservoirs are filled due to sediment movement. Sediment amount which is carried by a river depends on the river’s flow rate, inclination of its base and time. In this study, sediment estimations of Euphrates basin which was selected as the area for practice, is the largest basin in Turkey and contains its largest dams, based on classical formulations like Du Boys, Meyer-Peter-Muller, Schoklitsch, Shields and Garde-Albertson. Then, sediment values were estimated by using artificial neural networks (ANN) having a network architecture, which was developed by the authors. High correlation was observed between the values found by using a feed-forward and backpropagation and the observed values of ANN. This evidence, emphasizes how effective and efficient this method is, compared with classical methods. Design of reservoirs dead storages depends on realistic and reliable estimation of sediment yield. In this study, more realistic values have been obtained with ANN model compared with classical equations. Moreover, when sediment measurement cannot be conducted for a certain period, its amounts for the absent period may be estimated by using ANN technique with a little error.

[1]  Özgür Kisi,et al.  Predicting discharge capacity of triangular labyrinth side weir located on a straight channel by using an adaptive neuro-fuzzy technique , 2010, Adv. Eng. Softw..

[2]  Ashraf Ashour,et al.  Concrete breakout strength of single anchors in tension using neural networks , 2005, Adv. Eng. Softw..

[3]  N. Trustrum,et al.  Impacts of land use change on patterns of sediment flux in Weraamaia catchment, New Zealand , 2005 .

[4]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[5]  Hüsamettin Bulut,et al.  Trends in streamflow of the Euphrates basin, Turkey , 2008 .

[6]  R.E. Challis,et al.  Quantitative classification of adhesive bondline dimensions using Lamb waves and artificial neural networks , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  Kasim Yenigun,et al.  Reliability in dams and the effects of spillway dimensions on risk levels , 2007 .

[9]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[10]  CigizogluHikmet Kerem,et al.  Generalized regression neural network in modelling river sediment yield , 2006 .

[11]  Ahmet Baylar,et al.  GERİYE YAYILMA YAPAY SINIR AĞI KULLANILARAK YANAL SU ALMA YAPISINA YÖNELECEK OLAN SÜRÜNTÜ MADDESİ ORANININ BULUNMASI , 1999 .

[12]  R. Wheatcroft,et al.  River sediment flux and shelf sediment accumulation rates on the Pacific Northwest margin , 2005 .

[13]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[14]  Ian Flood,et al.  Neural Networks in Civil Engineering. I: Principles and Understanding , 1994 .

[15]  S. Luk,et al.  Water and sediment yield from a small catchment in the hilly granitic region, South China , 1997 .

[16]  Paki Turgut,et al.  Artificial Neural Network Approach to Predict Compressive Strength of Concrete through Ultrasonic Pulse Velocity , 2010 .

[17]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[18]  B. Kjerfve,et al.  Factors controlling sediment yield in a major South American drainage basin: the Magdalena River, Colombia , 2006 .

[19]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[20]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[21]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[22]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation for Rivers using Artificial Neural Networks and Sediment Rating Curves , 2002 .

[23]  Mahmud Güngör,et al.  Askı Madde Konsantrasyonu ve Miktarının Yapay Sinir Ağları ile Belirlenmesi , 2004 .

[24]  A. Yanmaz.  Applied water resources engineering , 2001 .

[25]  Xin-bao Zhang,et al.  Current changes of sediment yields in the upper Yangtze River and its two biggest tributaries, China , 2004 .

[26]  H. Raman,et al.  Multivariate modelling of water resources time series using artificial neural networks , 1995 .

[27]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation and Forecasting using Artificial Neural Networks , 2002 .

[28]  J. Poesen,et al.  Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models , 2005 .

[29]  S. D. Probert,et al.  Water and sediment movements in harbours , 2000 .

[30]  M. Duman,et al.  Surficial sediment distribution and net sediment transport pattern in İzmir Bay, western Turkey , 2004 .

[31]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .

[32]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[33]  Joos Vandewalle,et al.  Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin , 2003 .

[34]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[35]  A. Turgeon,et al.  Fuzzy Learning Decomposition for the Scheduling of Hydroelectric Power Systems , 1996 .

[36]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[37]  A. M. Davies,et al.  Processes influencing suspended sediment movement on the Malin–Hebrides shelf , 2002 .

[38]  Mahmut Bilgehan,et al.  A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches , 2011 .

[39]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[40]  Özgür Kisi,et al.  Constructing neural network sediment estimation models using a data-driven algorithm , 2008, Math. Comput. Simul..

[41]  Özgür Kisi,et al.  Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel , 2010, Adv. Eng. Softw..

[42]  Manish A. Kewalramani,et al.  Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks , 2006 .

[43]  James H. Garrett,et al.  Artificial Neural Networks for Civil Engineers: Fundamentals and Applications , 1997 .

[44]  Paul L. G. Vlek,et al.  Analysis of factors determining sediment yield variability in the highlands of northern Ethiopia , 2006 .

[45]  Emrah Doğan.  Katı Madde Konsantrasyonunun Yapay Sinir Ağlarını Kullanarak Tahmin Edilmesi , 2009 .

[46]  Vijay P. Singh,et al.  Tank Model for Sediment Yield , 2005 .

[47]  李幼升,et al.  Ph , 1989 .

[48]  Bayram Turgut,et al.  A back-propagation artificial neural network approach for three-dimensional coordinate transformation , 2010 .

[49]  D. Vericat,et al.  Sediment transport in a large impounded river: The lower Ebro, NE Iberian Peninsula , 2006 .

[50]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[51]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .